1
|
Bordbar M, Busico G, Sirna M, Tedesco D, Mastrocicco M. A multi-step approach to evaluate the sustainable use of groundwater resources for human consumption and agriculture. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 347:119041. [PMID: 37783086 DOI: 10.1016/j.jenvman.2023.119041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 09/11/2023] [Accepted: 09/17/2023] [Indexed: 10/04/2023]
Abstract
The rapid decline in both quality and availability of freshwater resources on our planet necessitates their thorough assessment to ensure sustainable usage. The growing demand for water in industrial, agricultural, and domestic sectors poses significant challenges to managing both surface and groundwater resources. This study tests and proposes a hybrid evaluation approach to determine Groundwater Quality Indices (GQIs) for irrigation (IRRI), seawater intrusion (SWI), and potability (POT), finalized to the spatial distribution of groundwater suitability involving water quality indicator along with hydrogeological and socio-economic factors. Mean Decrease Accuracy (MDA) and Information Gain Ratio (IGR) were used to state the importance of chosen factors such as level of groundwater above the sea, thickness of the aquifer, land cover, distance from coastline, silt soil content, recharge, distance from river and lagoons, depth to water table from ground, distance from agricultural wells, hydraulic conductivity, and lithology for each quality index, separately. The results of both methods showed that recharge is the most important parameter for GQIIRRI and GQIPOT, while the distance from the coastline and the rivers, are the most important for GQISWI. The spatial modelling of GQIIRRI and GQIPOT in the study area has been achieved applying three machine learning (ML) algorithms: the Boosted Regression Tree (BRT), the Random Forest (RF), and the Support Vector Machine (SVM). Validation results showed that RF has the highest prediction for GQIIRRI, while the SVM model has the highest prediction for the GQIPOT index. It is worth to mention that the future utilization and testing of new algorithms could produce even better results. Finally, GQIIRRI and GQIPOT were combined and compared using two combine and overlay methods to prepare a hybrid map of multi-GQIs. The results showed that 69% of the study area is suitable for irrigation and potable use, due to both geogenic and anthropogenic activities which contribute to make some water resources unsuitable for either use. Specifically, the northern, western, and eastern portions of the study area are in the "very high and high quality" classes while the southern portion shows "very low and low quality" classes. In conclusion, the developed map and approach can serve as a practical guide for enhancing groundwater management, identifying suitable areas for various uses and pinpointing regions requiring improved management practices.
Collapse
Affiliation(s)
- Mojgan Bordbar
- University of Campania "Luigi Vanvitelli", Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Caserta, Italy; Department of GIS/RS, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Gianluigi Busico
- University of Campania "Luigi Vanvitelli", Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Caserta, Italy; Department of Geology, Laboratory of Engineering Geology & Hydrogeology, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece.
| | - Maurizio Sirna
- University of Campania "Luigi Vanvitelli", Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Caserta, Italy
| | - Dario Tedesco
- University of Campania "Luigi Vanvitelli", Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Caserta, Italy; Osservatorio Vesuviano, National Institute of Geophysics and Volcanology, Via Diocleziano 328, Napoli, 80124, Italy
| | - Micol Mastrocicco
- University of Campania "Luigi Vanvitelli", Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Caserta, Italy
| |
Collapse
|
2
|
Ntona MM, Busico G, Mastrocicco M, Kazakis N. Coupling SWAT and DPSIR models for groundwater management in Mediterranean catchments. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 344:118543. [PMID: 37413730 DOI: 10.1016/j.jenvman.2023.118543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 06/26/2023] [Accepted: 06/26/2023] [Indexed: 07/08/2023]
Abstract
Groundwater is an essential natural resource and has a significant role in human and environmental health as well as in the economy. Management of subsurface storage remains an important option to meet the combined demands of humans and ecosystems. The increasing need to find multi-purpose solutions to address water scarcity is a global challenge. Thus, the interactions leading to surface runoff and groundwater recharge have received particular attention over the last decades. Additionally, new methods are developed to incorporate the spatial-temporal variation of recharge in groundwater modeling. In this study, groundwater recharge was spatiotemporally quantified using the Soil and Water Assessment Tool (SWAT) in the Upper Volturno-Calore hydrological basin in Italy and the results were compared with other two basins in Greece (Anthemountas and Mouriki). SWAT model was applied in actual and future projections (2022-2040) using the Representative Concentration Pathway (RCP) 4.5 emissions scenario to evaluate changes in precipitation and assess the future hydrologic conditions, along with, the Driving Force-Pressure-State-Impact-Response (DPSIR) framework that was applied in all the basins as a low-cost analysis of integrated physical, social, natural, and economic factors. According to the results, no significant variations in runoff are predicted in the Upper Volturno-Calore basin for the period 2020-2040 while the potential evapotranspiration percentage varies from 50.1% to 74.3% and infiltration around 5%. The limited primary data constitutes the main pressure in all sites and exaggerates the uncertainty of future projections.
Collapse
Affiliation(s)
- Maria Margarita Ntona
- Campania University "Luigi Vanvitelli", Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Via Vivaldi 43, 81100, Caserta, Italy; Aristotle University of Thessaloniki, Department of Geology, Laboratory of Engineering Geology & Hydrogeology, 54124, Thessaloniki, Greece
| | - Gianluigi Busico
- Campania University "Luigi Vanvitelli", Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Via Vivaldi 43, 81100, Caserta, Italy
| | - Micòl Mastrocicco
- Campania University "Luigi Vanvitelli", Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Via Vivaldi 43, 81100, Caserta, Italy
| | - Nerantzis Kazakis
- Aristotle University of Thessaloniki, Department of Geology, Laboratory of Engineering Geology & Hydrogeology, 54124, Thessaloniki, Greece.
| |
Collapse
|
3
|
Xia YY, de Seymour JV, Yang XJ, Zhou LW, Liu Y, Yang Y, Beck KL, Conlon CA, Mansell T, Novakovic B, Saffery R, Han TL, Zhang H, Baker PN. Hair and cord blood element levels and their relationship with air pollution, dietary intake, gestational diabetes mellitus, and infant neurodevelopment. Clin Nutr 2023; 42:1875-1888. [PMID: 37625317 DOI: 10.1016/j.clnu.2023.08.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 07/30/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023]
Abstract
BACKGROUND & AIMS Exposure to a range of elements, air pollution, and specific dietary components in pregnancy has variously been associated with gestational diabetes mellitus (GDM) risk or infant neurodevelopmental problems. We measured a range of pregnancy exposures in maternal hair and/or infant cord serum and tested their relationship to GDM and infant neurodevelopment. METHODS A total of 843 pregnant women (GDM = 224, Non-GDM = 619) were selected from the Complex Lipids in Mothers and Babies cohort study. Forty-eight elements in hair and cord serum were quantified using inductively coupled plasma-mass spectrometry analysis. Binary logistic regression was used to estimate the associations between hair element concentrations and GDM risk, while multiple linear regression was performed to analyze the relationship between hair/cord serum elements and air pollutants, diet exposures, and Bayley Scales of infant neurodevelopment at 12 months of age. RESULTS After adjusting for maternal age, BMI, and primiparity, we observed that fourteen elements in maternal hair were associated with a significantly increased risk of GDM, particularly Ta (OR = 9.49, 95% CI: 6.71, 13.42), Re (OR = 5.21, 95% CI: 3.84, 7.07), and Se (OR = 5.37, 95% CI: 3.48, 8.28). In the adjusted linear regression model, three elements (Rb, Er, and Tm) in maternal hair and infant cord serum were negatively associated with Mental Development Index scores. For dietary exposures, elements were positively associated with noodles (Nb), sweetened beverages (Rb), poultry (Cs), oils and condiments (Ca), and other seafood (Gd). In addition, air pollutants PM2.5 (LUR) and PM10 were negatively associated with Ta and Re in maternal hair. CONCLUSIONS Our findings highlight the potential influence of maternal element exposure on GDM risk and infant neurodevelopment. We identified links between levels of these elements in both maternal hair and infant cord serum related to air pollutants and dietary factors.
Collapse
Affiliation(s)
- Yin-Yin Xia
- Department of Obstetrics and Gynaecology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China; Department of Occupational and Environmental Hygiene, School of Public Health, Research Center for Medicine and Social Development, Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, China; Mass Spectrometry Center of Maternal Fetal Medicine, Chongqing Medical University, Chongqing, China
| | - Jamie V de Seymour
- School of Sport, Exercise and Nutrition, College of Health, Massey University, Auckland, New Zealand
| | - Xiao-Jia Yang
- Department of Occupational and Environmental Hygiene, School of Public Health, Research Center for Medicine and Social Development, Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, China
| | - Lin-Wei Zhou
- Department of Obstetrics and Gynaecology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yue Liu
- Department of Obstetrics and Gynaecology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China; Department of Occupational and Environmental Hygiene, School of Public Health, Research Center for Medicine and Social Development, Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, China; Mass Spectrometry Center of Maternal Fetal Medicine, Chongqing Medical University, Chongqing, China
| | - Yang Yang
- Department of Obstetrics and Gynaecology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China; Mass Spectrometry Center of Maternal Fetal Medicine, Chongqing Medical University, Chongqing, China
| | - Kathryn L Beck
- School of Sport, Exercise and Nutrition, College of Health, Massey University, Auckland, New Zealand
| | - Cathryn A Conlon
- School of Sport, Exercise and Nutrition, College of Health, Massey University, Auckland, New Zealand
| | - Toby Mansell
- Molecular Immunity, Murdoch Children's Research Institute, Melbourne, VIC, Australia; Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia
| | - Boris Novakovic
- Molecular Immunity, Murdoch Children's Research Institute, Melbourne, VIC, Australia; Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia
| | - Richard Saffery
- Molecular Immunity, Murdoch Children's Research Institute, Melbourne, VIC, Australia; Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia
| | - Ting-Li Han
- Mass Spectrometry Center of Maternal Fetal Medicine, Chongqing Medical University, Chongqing, China; Department of Obstetrics and Gynaecology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China; Institute of Life Sciences, Chongqing Medical University, Chongqing, China.
| | - Hua Zhang
- Department of Obstetrics and Gynaecology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China; Mass Spectrometry Center of Maternal Fetal Medicine, Chongqing Medical University, Chongqing, China.
| | - Philip N Baker
- College of Life Sciences, University of Leicester, Leicester, United Kingdom
| |
Collapse
|
4
|
Biswas T, Pal SC, Chowdhuri I, Ruidas D, Saha A, Islam ARMT, Shit M. Effects of elevated arsenic and nitrate concentrations on groundwater resources in deltaic region of Sundarban Ramsar site, Indo-Bangladesh region. MARINE POLLUTION BULLETIN 2023; 188:114618. [PMID: 36682305 DOI: 10.1016/j.marpolbul.2023.114618] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 01/09/2023] [Accepted: 01/12/2023] [Indexed: 06/17/2023]
Abstract
An attempt has been adopted to predict the As and NO3- concentration in groundwater (GW) in fast-growing coastal Ramsar region in eastern India. This study is focused to evaluate the As and NO3- vulnerable areas of coastal belts of the Indo-Bangladesh Ramsar site a hydro-geostrategic region of the world by using advanced ensemble ML techniques including NB-RF, NB-SVM and NB-Bagging. A total of 199 samples were collected from the entire study area for utilizing the 12 GWQ conditioning factors. The predicted results are certified that NB-Bagging the most suitable and preferable model in this current research. The vulnerability of As and NO3- concentration shows that most of the areas are highly vulnerable to As and low to moderately vulnerable to NO3. The reliable findings of this present study will help the management authorities and policymakers in taking preventive measures in reducing the vulnerability of water resources and corresponding health risks.
Collapse
Affiliation(s)
- Tanmoy Biswas
- Department of Geography, The University of Burdwan, Purba Bardhaman, West Bengal 713104, India
| | - Subodh Chandra Pal
- Department of Geography, The University of Burdwan, Purba Bardhaman, West Bengal 713104, India.
| | - Indrajit Chowdhuri
- Department of Geography, The University of Burdwan, Purba Bardhaman, West Bengal 713104, India
| | - Dipankar Ruidas
- Department of Geography, The University of Burdwan, Purba Bardhaman, West Bengal 713104, India
| | - Asish Saha
- Department of Geography, The University of Burdwan, Purba Bardhaman, West Bengal 713104, India
| | | | - Manisa Shit
- Department of Geography, Raiganj University, Raiganj, Uttar Dinajpur, West Bengal 733134, India
| |
Collapse
|
5
|
Nafouanti MB, Li J, Nyakilla EE, Mwakipunda GC, Mulashani A. A novel hybrid random forest linear model approach for forecasting groundwater fluoride contamination. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:50661-50674. [PMID: 36800089 DOI: 10.1007/s11356-023-25886-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 02/07/2023] [Indexed: 02/18/2023]
Abstract
Groundwater quality in the Datong basin is threatened by high fluoride contamination. Laboratory analysis is a standard method for estimating groundwater quality parameters, which is expensive and time-consuming. Therefore, this paper proposes a hybrid random forest linear model (HRFLM) as a novel approach for estimating groundwater fluoride contamination. Light gradient boosting (LightGBM), random forest (RF), and extreme gradient boosting (Xgboost) were also employed in comparison with HRFLM for predicting fluoride contamination in groundwater. 202 groundwater samples were collected to draw up the performance capability of several models in forecasting subsurface water fluoride contamination. The performance of the models was assessed utilizing the receiver operating characteristic (ROC) area under the curve (AUC) and the confusion matrix (CM). The CM results reveal that with nine predictor variables, the hybrid HRFLM achieved an accuracy of 95%, outperforming the Xgboost, LightGBM, and RF models, which attained 88%, 88%, and 85%, respectively. Likewise, the AUC results of the hybrid HRFLM show high performance with an AUC of 0.98 compared to Xgboost, LightGBM, and RF, which achieved an AUC of 0.95, 0.90, and 0.88, respectively. The study demonstrates that the HRFLM can be applied as an advanced approach for groundwater fluoride contamination prediction in the Datong basin and could be adopted in various areas facing a similar challenge.
Collapse
Affiliation(s)
- Mouigni Baraka Nafouanti
- State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan, 430074, China.
| | - Junxia Li
- State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan, 430074, China.,China Laboratory of Basin Hydrology and Wetland Eco-restoration, China University of Geosciences, Wuhan, 430074, China
| | - Edwin E Nyakilla
- Department of Petroleum Engineering, Faculty of Earth Resources, China University of Geosciences, Wuhan, 430074, China
| | - Grant Charles Mwakipunda
- Department of Petroleum Engineering, Faculty of Earth Resources, China University of Geosciences, Wuhan, 430074, China
| | - Alvin Mulashani
- Department of Geosciences and Mining Technology, College of Engineering and Technology, Mbeya University of Science and Technology, Box 131, Mbeya, Tanzania
| |
Collapse
|
6
|
Mendes RG, do Valle Junior RF, de Melo Silva MMAP, de Morais Fernandes GH, Fernandes LFS, Fernandes ACP, Pissarra TCT, de Melo MC, Valera CA, Pacheco FAL. A partial least squares-path model of environmental degradation in the Paraopeba River, for rainy seasons after the rupture of B1 tailings dam, Brumadinho, Brazil. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 851:158248. [PMID: 36028023 DOI: 10.1016/j.scitotenv.2022.158248] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 08/19/2022] [Accepted: 08/19/2022] [Indexed: 06/15/2023]
Abstract
The present study aimed to investigate the rupture of B1 tailings dam of Córrego do Feijão mine, which drastically affected the region of Brumadinho (Minas Gerais, Brazil). The contamination of water resources reached 155.3 km from the dam site. In the river channel, high concentrations of Mn, Al, As and Fe were detected and correlated to the spillage of the tailings in the river. The presence of the tailings also affected the chlorophyll-a content in the water, as well as the reflectance of riparian forests. With the increase of metal(oid) concentrations above permitted levels, water management authorities suspended the use of Paraopeba River as resource in the impacted areas, namely the drinking water supply to the Metropolitan region of Belo Horizonte. This study aimed to evaluate possible links between tailings distribution, river water quality, and environmental degradation, which worked as latent variables in partial least squares regression models. The latent variables were represented by numerous physical and chemical parameters of water and sediment, measured four times in 22 locations during the rainy season of 2019, in addition to stream flow and to NDVI evaluated in satellite images processed daily. The modeling results suggested a relationship between river flow turbulence and increased arsenic release from sand fractions, as well as desorption of Mn from metal oxides, both representing causes of water quality reduction. They also revealed increasing iron concentrations affecting the forest NDVI (greening), which was interpreted as environmental degradation. The increase of chlorophyll-a concentrations (related with turbidity decreases), as well as the increase of river flows (responsible for dilution effects), seemed to work out as attenuators of degradation. Although applied to a specific site, our modeling approach can be transposed to equivalent dam failures and climate contexts, helping water resource management authorities to decide upon appropriate recovery solutions.
Collapse
Affiliation(s)
- Rafaella Gouveia Mendes
- Instituto Federal do Triângulo Mineiro (IFTM), Campus Uberaba, Laboratório de Geoprossessamento, Uberaba, MG 38064-790, Brazil
| | - Renato Farias do Valle Junior
- Instituto Federal do Triângulo Mineiro (IFTM), Campus Uberaba, Laboratório de Geoprossessamento, Uberaba, MG 38064-790, Brazil.
| | | | | | - Luís Filipe Sanches Fernandes
- Centro de Investigação e Tecnologias Agroambientais e Biológicas (CITAB), Universidade de Trás-os-Montes e Alto Douro (UTAD), Ap. 1013, 5001-801 Vila Real, Portugal.
| | - António Carlos Pinheiro Fernandes
- Centro de Recursos Naturais e Ambiente (CERENA/FEUP), Faculdade de Engenharia, Universidade do Porto, Dr. Roberto Frias st., Porto 4200-465, Portugal.
| | - Teresa Cristina Tarlé Pissarra
- Faculdade de Ciências Agrárias e Veterinárias, Universidade Estadual Paulista (UNESP), Via de Acesso Prof. Paulo Donato Castellane, s/n, Jaboticabal, SP 14884-900, Brazil.
| | - Marília Carvalho de Melo
- Secretaria de Estado de Meio Ambiente e Desenvolvimento Sustentável, Cidade Administrativa do Estado de Minas Gerais, Rodovia João Paulo II, 4143 Bairro Serra Verde - Belo Horizonte - Minas Gerais, Brazil.
| | - Carlos Alberto Valera
- Coordenadoria Regional das Promotorias de Justiça do Meio Ambiente das Bacias dos Rios Paranaíba e Baixo Rio Grande, Rua Coronel Antônio Rios, 951, Uberaba, MG 38061-150, Brazil.
| | - Fernando António Leal Pacheco
- Centro de Química de Vila Real (CQVR), Universidade de Trás-os-Montes e Alto Douro (UTAD), Ap. 1013, 5001-801 Vila Real, Portugal.
| |
Collapse
|
7
|
Costache R, Arabameri A, Costache I, Crăciun A, Md Towfiqul Islam AR, Abba SI, Sahana M, Pham BT. Flood susceptibility evaluation through deep learning optimizer ensembles and GIS techniques. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 316:115316. [PMID: 35598454 DOI: 10.1016/j.jenvman.2022.115316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 03/24/2022] [Accepted: 05/12/2022] [Indexed: 06/15/2023]
Abstract
It is difficult to predict and model with an accurate model the floods, that are one of the most destructive risks across the earth's surface. The main objective of this research is to show the prediction power of three ensemble algorithms with respect to flood susceptibility estimation. These algorithms are: Iterative Classifier Optimizer - Alternating Decision Tree - Frequency Ratio (ICO-ADT-FR), Iterative Classifier Optimizer - Deep Learning Neural Network - Frequency Ratio (ICO-DLNN-FR) and Iterative Classifier Optimizer - Multilayer Perceptron - Frequency Ratio (ICO-MLP-FR). The first stage of the manuscript consisted of the collection and processing of the geodatabase needed in the present study. The geodatabase comprises a number of 14 flood predictors and 132 known flood locations. The Correlation-based Feature Selection (CFS) method was used in order to assess the prediction capacity of the 14 predictors in terms of flood susceptibility estimation. The training and validation of the three ensemble models constitute the next stage of the scientific workflow. Several statistical metrics and ROC curve method were involved in the evaluation of the model's performance and accuracy. According to ROC curves all the models achieved high performances since their AUC had values above 0.89. ICO-DLNN-FR proved to be the most accurate model (AUC = 0.959). The outcomes of the study can be used to guide future flood risk management and sustainable land-use planning in the designated area.
Collapse
Affiliation(s)
- Romulus Costache
- Department of Civil Engineering, Transilvania University of Brasov, 5, Turnului Str, 500152, Brasov, Romania; Danube Delta National Institute for Research and Development,165 Babadag Street, 820112, Tulcea, Romania.
| | - Alireza Arabameri
- Department of Geomorphology, Tarbiat Modares University, Tehran, 36581-17994, Iran.
| | - Iulia Costache
- Faculty of Geography, University of Bucharest, Bucharest, 010041, Romania.
| | - Anca Crăciun
- Danube Delta National Institute for Research and Development,165 Babadag Street, 820112, Tulcea, Romania.
| | | | - S I Abba
- Interdisciplinary Research Center for Membrane and Water Security, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia.
| | - Mehebub Sahana
- Department of Geography, University of Manchester, United Kingdom.
| | - Binh Thai Pham
- Geotechnical Engineering and Artificial Intelligence research group (GEOAI), University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Ha Noi, 100000, Viet Nam.
| |
Collapse
|
8
|
Islam ARMT, Pal SC, Chowdhuri I, Salam R, Islam MS, Rahman MM, Zahid A, Idris AM. Application of novel framework approach for prediction of nitrate concentration susceptibility in coastal multi-aquifers, Bangladesh. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 801:149811. [PMID: 34467937 DOI: 10.1016/j.scitotenv.2021.149811] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Revised: 07/31/2021] [Accepted: 08/17/2021] [Indexed: 06/13/2023]
Abstract
This study aims to construct a novel framework approach for predicting and mapping nitrate concentration susceptibility in the coastal multi-aquifers of Bangladesh by coupling the K-fold cross-validation method and novel ensemble learning algorithms, including Boosting, Bagging and Random Forest (RF). In total, 286 nitrate sampling sites were employed in the model work. The dataset was demarcated into a 75:25 ratio for model construction (75% 3-fold ≅ 214 sites) and (25% 1-fold ≅ 72 sites) for model validation using the 4-fold cross-validation schemes. A total of 14 groundwater causative factors including salinity, depth, pH, EC, As, HCO3-, F-, Cl-, SO42-, PO42-, Na+, K+, Mg2+, and Ca2+ were adopted for the construction of the proposed models. OneR relative importance model was employed to choose and rank critical factors for spatial nitrate modeling. The results showed that depth, pH and As are the most influential causative factors in the elevated nitrate concentration in groundwater. Based on the model assessment criteria such as receiver operating characteristic (ROC)'s AUC (area under curve), sensitivity, specificity, accuracy, precession, F score, and Kappa coefficient, the Boosting model outperforms others (r = 0.92, AUG ≥ 0.90) in mapping nitrate concentration susceptibility, followed by Bagging and RF models. The results of mapping nitrate concentration also demonstrated that the south-central and western regions had an elevated amount of nitrate content than other regions due to depth variation in the study area. During our sampling campaign, we observed hundreds of fish hatcheries operation, a fish landing center and aquaculture farms which are the reasons for overexploitation and excessive agrochemicals used in the study area. Thus, the dependability of ensemble learning modeling verifies the effectiveness and applicability of the proposed novel approach for decision-makers in groundwater pollution management at the local and regional levels.
Collapse
Affiliation(s)
| | - Subodh Chandra Pal
- Department of Geography, The University of Burdwan, Bardhaman 713104, West Bengal, India.
| | - Indrajit Chowdhuri
- Department of Geography, The University of Burdwan, Bardhaman 713104, West Bengal, India
| | - Roquia Salam
- Department of Disaster Management, Begum Rokeya University, Rangpur 5400, Bangladesh
| | - Md Saiful Islam
- Department of Soil Science, Patuakhali Science and Technology University, Dumki, Patuakhali 8602, Bangladesh
| | - Md Mostafizur Rahman
- Department of Environmental Sciences, Jahangirnagar University, Dhaka 1342, Bangladesh
| | - Anwar Zahid
- Bangladesh Water Development Board (BWDB), Dhaka, Bangladesh
| | - Abubakr M Idris
- Department of Chemistry, College of Science, King Khalid University, Abha 61431, Saudi Arabia; Research Center for Advanced Materials Science (RCAMS), King Khalid University, Abha, Saudi Arabia
| |
Collapse
|